Analyzing Call Transcripts with LLMs

Conversational intelligence and call transcript analysis are valuable tools for businesses aiming to enhance communication by utilizing technologies such as natural language processing and sentiment analysis to evaluate not just the content of conversations, but also the tone, context, and emotions involved. These tools are widely applied in sectors like customer service, sales, and healthcare to identify trends and areas for improvement, thus improving customer experiences and business performance. The text outlines a step-by-step guide for using Refuel's platform to analyze large volumes of call transcripts by leveraging large language models (LLMs) to identify key topics and extract relevant data points, which is particularly beneficial for deriving insights from sales calls. The process involves uploading transcript datasets, creating analysis tasks, defining fields for inquiry, reviewing outputs for accuracy, and providing feedback to refine results. Optional steps include adding more questions for detailed insights and fine-tuning models for efficiency, culminating in deploying a workflow for real-time analysis and ongoing insights, which supports informed decision-making based on sales call data.